The present invention relates to acoustic physiological monitoring and, more particularly, physiological parameter estimation in acoustic physiological monitoring.
Real-time physiological monitoring can be helpful in maintaining the health of people as they go about their daily lives. For example, real-time physiological monitoring can be used to rapidly detect heart or respiratory ailments.
Real-time physiological monitoring often invokes the body sound method, which is sometimes called auscultation. In the body sound method, an acoustic transducer mounted on the body of the person captures and acquires an acoustic signal recording body sounds, such as heart or respiration sounds. Once the acoustic signal has been generated, a heartbeat or respiration sequence may be identified in the acoustic signal and heart or respiration rate may be estimated. Health status information based on the heart or respiration rate estimate may then be outputted locally to the monitored person or remotely to a clinician.
Many known real-time physiological monitoring systems analyze an acoustic signal in the time or frequency domain to estimate heart or respiration rate. For example, a time domain technique for estimating heart rate generally uses autocorrelation to identify the fundamental periodicity in the acoustic signal within the frequency band for heart sounds by analyzing recurring energy peaks. The recurring energy peaks are recognized as heartbeats and heart rate is estimated using the distance between adjacent energy peaks. A frequency domain technique for estimating heart rate generally evaluates spectral density of different frequencies within the frequency band for heart sounds and identifies the most significant frequency as the heartbeat frequency, from which heart rate is estimated.
Both of these conventional approaches to estimating heart rate work well when the signal-to-noise ratio is sufficiently high, but are prone to problems in the presence of large noise. An acoustic signal that records body sounds can be disrupted by several types of large noise, including short-term, high amplitude noise introduced by impulse events such as talking, coughing or sneezing. Large noise can mask the heartbeat, resulting in erroneous heart rate estimation and outputting of erroneous health status information. In turn, reliance on erroneous health status information can have serious adverse consequences on the health of the monitored person. For example, such information can lead the person or his or her clinician to improperly diagnose health status and cause the person to undergo treatment that is not medically indicated or forego treatment that is medically indicated.
Some known approaches attempt to combat noise-induced heart rate estimation error by trying to remove large noise from the acoustic signal, such as by using a reference microphone to measure environmental noise and attempting to cancel the noise through differentiation. Other known approaches attempt to combat such error by isolating noisy portions of the acoustic signal and excluding them when estimating heart rate. However, these noise handling approaches add substantial complexity to the physiological monitoring system and at best only offer piecemeal solutions.